You generate embeddings using all-MiniLM-L12-v2 by loading the model with a sentence embedding library and passing text inputs through it to obtain fixed-length vectors. In most setups, developers use a high-level API that handles tokenization, batching, and pooling internally. The typical workflow is simple: normalize your text (trim whitespace, remove obvious noise), send a list of sentences or short paragraphs to the model, and receive a list of numerical vectors. These vectors represent semantic meaning and can be compared using cosine similarity or inner product.
In a production pipeline, embedding generation is usually split into two phases: offline indexing and online querying. Offline, you embed your document corpus in batches and store the resulting vectors. Online, you embed each user query in real time and search for the nearest vectors. Because all-MiniLM-L12-v2 is lightweight, query-time embedding can often be done on CPU with acceptable latency. You should also normalize embeddings (for example, L2 normalization) so that similarity metrics behave consistently across queries.
Once embeddings are generated, they become useful only when stored and searched efficiently. This is where a vector database such as Milvus or Zilliz Cloud fits naturally. You insert embeddings along with metadata like document IDs or categories, then perform similarity search at query time. A typical flow looks like: embed(document) → insert(vector, metadata) and embed(query) → search(topK). This separation of embedding and retrieval makes the system easier to debug and optimize, especially when you want to experiment with chunk sizes or filtering rules without touching the model itself.
For more information, click here: https://zilliz.com/ai-models/all-minilm-l12-v2